Databricks Certified Associate Developer for Apache Spark Practice Test Questions, Exam Dumps

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Certified Associate Developer for Apache Spark Databricks Practice Test Questions and Exam Dumps

Question No 1:

Which of the following statements correctly describes the Spark driver in the context of a Spark application?

A. The Spark driver is responsible for performing all execution in all execution modes – it is the entire Spark application.
B. The Spark driver is fault-tolerant – if it fails, it will recover the entire Spark application.
C. The Spark driver is the coarsest level of the Spark execution hierarchy – it is synonymous with the Spark application.
D. The Spark driver is the program space in which the Spark application’s main method runs, coordinating the entire Spark application.
E. The Spark driver is horizontally scaled to increase the overall processing throughput of a Spark application.

Correct Answer:

D. The Spark driver is the program space in which the Spark application’s main method runs, coordinating the entire Spark application.

Explanation:

The Spark driver plays a central role in the functioning of a Spark application. It is responsible for overseeing the execution of the entire job and coordinates the distribution of tasks across various worker nodes in the cluster. Here’s a breakdown of the key functions and characteristics:

Coordination Role: The Spark driver is the entry point for the Spark application. It contains the main() method (or equivalent), where the program begins execution. The driver coordinates the execution flow, interacts with the cluster manager (like YARN or Mesos), and schedules tasks on worker nodes (executors).
Job Scheduling and Execution: The driver manages the stages of the job, dividing it into smaller tasks and distributing them to executors. It is responsible for managing the overall state of the computation, including the distribution of data, tracking intermediate results, and determining when tasks are complete.
Cluster Interaction: While the executors are responsible for carrying out the tasks, the driver communicates with them to ensure tasks are being executed properly. The driver also tracks the status of tasks and handles failures by rescheduling tasks when necessary.
Fault Tolerance: Unlike some statements suggest, the driver itself is not fault-tolerant. If the driver fails, the entire Spark application will fail, requiring a restart. This contrasts with the workers (executors), which can recover from failure by rerunning tasks on other nodes.
No Horizontal Scaling: The Spark driver does not scale horizontally like executors. It is typically a single entity, unlike the executors which can be scaled horizontally to improve performance and throughput.

Thus, Option D correctly describes the Spark driver as the program space where the main method runs and manages the coordination of the Spark application, while other options either misinterpret the driver's role or describe characteristics of other components in Spark's architecture.

Question No 2:

What is the correct relationship between nodes and executors in a distributed computing environment, and which of the following statements best describes this relationship?

A. Executors and nodes are not related.
B. A node is a processing engine running on an executor.
C. An executor is a processing engine running on a node.
D. There are always the same number of executors and nodes.
E. There are always more nodes than executors.

Answer: C. An executor is a processing engine running on a node.

Explanation:

In distributed computing frameworks such as Apache Spark or Hadoop, the architecture typically involves nodes and executors working together to perform parallel processing tasks. To understand the correct relationship, let's break down these terms:

Node: A node refers to a physical or virtual machine in a cluster that contributes to the overall computation process. It serves as the computing unit within the distributed system and may contain multiple cores or CPUs to handle parallel tasks.
Executor: An executor is a process launched on a node that runs computation tasks and stores data for the application. It is a key part of managing and executing tasks across nodes. In systems like Spark, an executor is responsible for executing a subset of the tasks in a job and returning the results.

The correct answer is C: "An executor is a processing engine running on a node." This means that each node in a cluster can run one or more executors depending on the available resources (like CPU cores) on that node. Executors are responsible for the execution of the application code and for managing the distributed tasks assigned to them.

Why other options are incorrect:

A is wrong because executors and nodes are indeed related. Executors run on nodes, making them directly linked in a distributed system.
B is incorrect because a node is not a processing engine. Instead, it serves as the physical/virtual machine that runs the executor.
D and E are incorrect because the number of executors and nodes in a cluster is not fixed; there can be multiple executors running on a single node, and the number of nodes can vary based on the cluster size and workload.

Thus, understanding that executors run on nodes is crucial for effectively managing distributed computing workloads.

Question No 3:

What will happen if there are more slots available than the number of tasks in a Spark job?

A. The Spark job will likely not run as efficiently as possible.
B. The Spark application will fail, as there must be at least as many tasks as there are slots.
C. Some executors will shut down and allocate all slots on larger executors first.
D. More tasks will be automatically generated to ensure all slots are being used.
E. The Spark job will use just one single slot to perform all tasks.

Detailed Question with Answer and Explanation:

In Apache Spark, a job is broken down into tasks that are scheduled for execution on a set of available executors. Each executor can run a certain number of tasks simultaneously, determined by the number of available slots. If there are more slots (available execution capacity) than tasks (the amount of work to be done), what will happen?

Answer: The correct answer is A: The Spark job will likely not run as efficiently as possible.

Explanation:

When there are more slots than tasks in a Spark job, it means there is unused capacity in the system. Here's why:

What is a Slot in Spark? A "slot" in Spark refers to a unit of computational capacity within an executor. Each task in a Spark job is assigned to a slot on an executor. The number of slots determines how many tasks can run concurrently in that executor.
Scenario with More Slots than Tasks: If there are more slots than tasks, some of the available slots will remain idle, as there aren't enough tasks to fill them. While Spark can utilize these available slots, the overall computational resources are underutilized, which leads to inefficiency. The job will complete, but it won’t make the best use of the system's resources, potentially slowing down processing time for large jobs.
Why is Option A Correct? The job will likely not run as efficiently as possible because the extra capacity provided by the slots is unused. While the job can still complete, the time it takes to execute could be longer than expected due to resource imbalance or poor parallelization.
Why the Other Options are Incorrect:

Option B: Spark does not require a one-to-one relationship between tasks and slots. Having more slots than tasks won't cause the application to fail.
Option C: Executors do not shut down just because there are more slots than tasks. Executors are managed based on task demand.
Option D: Spark does not automatically generate more tasks to match available slots unless specifically configured for more tasks (e.g., through dynamic allocation or repartitioning).
Option E: Spark will not use only one slot; it will distribute tasks as available slots allow. However, unused slots remain idle.

Thus, in the scenario where there are more slots than tasks, the Spark job runs but may not be utilizing all its resources efficiently, making the process slower than optimal.

Question No 4:

In the Spark execution hierarchy, which of the following represents the most granular level of execution?

A. Task
B. Executor
C. Node
D. Job
E. Slot

Answer:

The most granular level of the Spark execution hierarchy is A. Task.

Explanation:

Apache Spark's execution model is designed around a distributed computing architecture where jobs are divided into smaller units of work. These units are processed in parallel across a cluster of machines. To understand the granularity of Spark’s execution hierarchy, it is essential to break down the key components and their roles:

A. Task:
A task is the smallest unit of execution in Spark. It represents a single computation that works on a partition of data. Each task operates on a partition of the data in parallel and can be processed independently. The task is the most granular level because it is where the actual computation happens—whether it's applying a transformation to a DataFrame or performing an action on the data. Tasks are scheduled by the Spark scheduler and executed by workers on the cluster. Tasks are also the unit of failure; if a task fails, it can be retried independently of other tasks.
B. Executor:
An executor is a JVM process that runs on each node in the Spark cluster. Executors are responsible for executing tasks and returning the results. An executor is responsible for managing the life cycle of tasks, holding the data in memory, and communicating with the driver. While executors are critical for task execution, they are a higher-level abstraction compared to tasks, and thus not as granular.
C. Node:
A node is a machine in the Spark cluster that runs one or more executors. A node can contain multiple executors, each of which can handle multiple tasks. While nodes are crucial in determining the physical layout of the cluster, they are a much broader concept and are not as granular as tasks.
D. Job:
A job is a high-level Spark computation consisting of multiple stages. A job is typically triggered by an action (e.g., collect(), count(), save()). While a job contains multiple stages and tasks, it is not as granular as a task, as it encompasses a larger unit of execution.
E. Slot:
A slot refers to the resources available within an executor to run tasks. The term "slot" typically refers to the ability of an executor to run a task, but it is not a concept at the execution level itself. Slots are more about resource management rather than execution granularity.

Thus, tasks represent the most granular level of execution in the Spark execution hierarchy. Spark executes computations by breaking jobs down into tasks, which are processed on different nodes by executors. The task is the smallest, indivisible unit of execution, and understanding this concept is essential for optimizing Spark applications, especially in distributed environments.

Question No 5:

Which of the following statements about Spark jobs is incorrect? Provide the correct explanation for each option.

A. Jobs are broken down into stages.
B. There are multiple tasks within a single job when a DataFrame has more than one partition.
C. Jobs are collections of tasks that are divided up based on when an action is called.
D. There is no way to monitor the progress of a job.
E. Jobs are collections of tasks that are divided based on when language variables are defined.

Correct Answer: D. There is no way to monitor the progress of a job.

Explanation:

In Spark, a job is a high-level unit of work that consists of a series of operations, typically initiated by an action like collect(), save(), or count(). When Spark performs a job, it breaks the job into smaller units, often referred to as stages, and each stage is further divided into smaller tasks.

Here’s a breakdown of why each statement is either correct or incorrect:

A. Jobs are broken down into stages.
Correct. A job in Spark is divided into stages, which are further divided into tasks. These stages are based on the shuffling of data, and each stage represents a set of operations that can be executed without shuffling.
B. There are multiple tasks within a single job when a DataFrame has more than one partition.
Correct. A task in Spark corresponds to a single unit of work applied to a partition of data. If a DataFrame has multiple partitions, there will be multiple tasks within a single job, each task operating on one partition of the data.
C. Jobs are collections of tasks that are divided up based on when an action is called.
Correct. Spark jobs are initiated when an action (e.g., collect(), count(), etc.) is called. Each job is divided into stages and tasks, and these tasks are executed in parallel.
D. There is no way to monitor the progress of a job.
Incorrect. This statement is false because Spark provides a Web UI to monitor the progress of jobs. The UI provides detailed insights into the status of stages, tasks, and overall progress of the job.
E. Jobs are collections of tasks that are divided based on when language variables are defined.
Incorrect/Confusing. This statement is misleading and not accurate. The division of jobs into tasks is not related to when language variables are defined but rather based on the operations that need to be performed on the data, particularly when shuffling or repartitioning happens.

In summary, D is the incorrect statement because Spark does allow users to monitor job progress through its Web UI, which provides valuable insights into the performance and stages of the job execution.

Question No 6:

Which of the following DataFrame operations in Apache Spark is most likely to result in a shuffle, and why?

A. DataFrame.join()
B. DataFrame.filter()
C. DataFrame.union()
D. DataFrame.where()
E. DataFrame.drop()

Answer:

The operation most likely to result in a shuffle is A. DataFrame.join().

Explanation:

In Apache Spark, a shuffle occurs when data needs to be redistributed across different nodes in a cluster to perform certain operations. This can lead to high overhead because it requires data to be moved across the network. A shuffle is typically triggered when an operation requires the data to be reorganized based on some key or grouping, which is not already aligned across partitions.

Let's break down the operations listed:

A. DataFrame.join(): This operation is the most likely to cause a shuffle because it often requires matching keys from different partitions. For example, if two DataFrames are being joined based on a common column (e.g., a key column), Spark needs to ensure that rows with the same key are present in the same partition for an efficient join. If the key is not already distributed across partitions in a way that supports this, Spark will need to shuffle the data to align the key values, leading to a shuffle.
B. DataFrame.filter(): The filter() operation does not inherently require a shuffle because it simply removes rows that do not satisfy a given condition. The data remains within the same partition, and no movement of data is necessary unless there is some complex transformation involved that requires repartitioning. Therefore, filter() is unlikely to cause a shuffle.
C. DataFrame.union(): The union() operation appends the rows of one DataFrame to another. If both DataFrames are already partitioned in a similar way (i.e., same partitioning scheme), a shuffle is not necessary. However, if they have different partitioning schemes, a shuffle might occur to align the partitions, though it’s not as common as with a join.
D. DataFrame.where(): Like filter(), where() also applies a condition to the rows of a DataFrame. Since this operation does not require reorganization of the data across partitions, it typically does not lead to a shuffle.
E. DataFrame.drop(): This operation removes a column from the DataFrame. Since the column removal doesn't require repartitioning the data or redistributing the rows, a shuffle does not occur with the drop() operation.

Thus, the join() operation is the most likely to trigger a shuffle because it involves redistributing data across partitions based on the join keys. This is a critical aspect of distributed computing in Spark, as shuffling can significantly impact performance. Understanding when and why a shuffle occurs is essential for optimizing Spark applications, as shuffles can be expensive in terms of time and resources.

Question No 7:

In Apache Spark, the default value of the configuration parameter spark.sql.shuffle.partitions is 200. What does this default value mean for the behavior of Spark DataFrames during operations that involve shuffling data?

A. By default, all DataFrames in Spark will be split to perfectly fill the memory of 200 executors.
B. By default, new DataFrames created by Spark will be split to perfectly fill the memory of 200 executors.
C. By default, Spark will only read the first 200 partitions of DataFrames to improve speed.
D. By default, all DataFrames in Spark, including existing DataFrames, will be split into 200 unique segments for parallelization.
E. By default, DataFrames will be split into 200 unique partitions when data is being shuffled.

Correct Answer:

E. By default, DataFrames will be split into 200 unique partitions when data is being shuffled.

Explanation:

In Apache Spark, data is processed in parallel across a cluster using a distributed computation model. One key operation that affects performance is shuffling, where data is reorganized across different partitions to satisfy certain transformations such as joins, groupBy, or aggregations. The parameter spark.sql.shuffle.partitions determines how many partitions the data will be divided into during the shuffle stage of the computation.

By default, the value of spark.sql.shuffle.partitions is set to 200. This means that, after a shuffle operation (such as a groupBy or join), Spark will attempt to repartition the data into 200 partitions. This repartitioning ensures that the shuffle operation can be parallelized effectively across the available executors, and it directly impacts the memory and computation overhead during the shuffle process.

It is important to note that this parameter affects the number of partitions Spark will use when shuffling data, not the number of partitions in the original DataFrame or RDD. Therefore, regardless of how many partitions an initial DataFrame has, the shuffle stage will split it into 200 partitions by default, unless you explicitly configure a different value for spark.sql.shuffle.partitions.

While the value of 200 is reasonable for many workloads, it is often adjusted based on the size of the data being processed and the available cluster resources. Increasing the number of shuffle partitions can reduce the size of each partition, which might help reduce the memory overhead during shuffling, but it can also increase the overhead from task scheduling and managing more partitions. Conversely, reducing the number of shuffle partitions might help with performance in scenarios where fewer, larger partitions are more efficient.

Therefore, Option E is the correct description of what the default setting means.

Question No 8:

Which of the following statements provides the most accurate description of lazy evaluation in programming?

A. None of these options describe lazy evaluation.
B. A process is lazily evaluated if its execution does not start until it is triggered by some action.
C. A process is lazily evaluated if its execution does not start until it is forced to display a result to the user.
D. A process is lazily evaluated if its execution does not start until it reaches a specified date and time.
E. A process is lazily evaluated if its execution does not start until it has finished compiling.

Correct Answer:

B. A process is lazily evaluated if its execution does not start until it is triggered by some action.

Explanation:

Lazy evaluation is a programming technique in which an expression is not evaluated until its value is actually needed. This approach can optimize performance by avoiding unnecessary computations, which is particularly useful in cases involving large data sets, infinite sequences, or computations that might never be used.

Option B provides the most accurate description of lazy evaluation. It highlights that the process does not start execution until it is triggered by an action. This "trigger" is often an explicit call, a function, or a demand for the result of an expression. By not evaluating until required, lazy evaluation ensures that computations are only performed when absolutely necessary.

For example, in functional programming languages like Haskell, lists can be infinite, but individual elements are only computed when requested. In this case, the elements of the list are lazily evaluated, meaning the system only computes the next element when the program requests it, not before.

Option A is incorrect because lazy evaluation is indeed a well-defined concept in programming, and one of the listed options does describe it.
Option C inaccurately links lazy evaluation to displaying results to the user, which is not the primary trigger for lazy evaluation.
Option D describes a time-based evaluation, which is unrelated to the concept of lazy evaluation.
Option E connects lazy evaluation with the compilation process, but lazy evaluation typically happens during program execution, not during compilation.

Thus, Option B is the best description, as it focuses on the fact that execution is delayed until explicitly needed, which is the core of lazy evaluation.

Question No 9:

Which of the following operations on a DataFrame in Apache Spark is classified as an action? Choose the correct answer and provide an explanation for why the chosen operation is an action.

A. DataFrame.drop()

B. DataFrame.coalesce()

C. DataFrame.take() D. DataFrame.join()

E. DataFrame.filter()

Answer: The correct answer is C. DataFrame.take().

Explanation:

In Apache Spark, operations on DataFrames can be categorized into two types: transformations and actions.

Transformations are operations that return a new DataFrame and are lazy. This means that they do not immediately execute when invoked. Instead, Spark builds an execution plan and only performs the actual computation when an action is called. Examples of transformations include operations like DataFrame.filter(), DataFrame.drop(), DataFrame.coalesce(), and DataFrame.join(). These operations modify or transform the data without triggering execution, and Spark will optimize the execution plan as needed.
Actions, on the other hand, trigger the execution of the computation. These operations return a value (or a collection) and force the evaluation of the DataFrame. When an action is called, Spark actually performs the computation defined by the transformations in the execution plan. The take() operation is an example of an action. It returns the first n rows of the DataFrame as an array, causing Spark to evaluate and process the data and return the result.

Now, let's break down the options:

A. DataFrame.drop() is a transformation that removes columns from the DataFrame, returning a new DataFrame without those columns.
B. DataFrame.coalesce() is a transformation that reduces the number of partitions in the DataFrame, optimizing how data is stored and distributed, but doesn't trigger computation.
C. DataFrame.take() is an action that triggers the execution of the transformations applied to the DataFrame. It retrieves a sample of the data (first n rows), causing Spark to evaluate the query and return the result.
D. DataFrame.join() is a transformation that combines two DataFrames based on a common column, creating a new DataFrame.
E. DataFrame.filter() is a transformation that filters rows based on a specified condition, returning a new DataFrame.

In summary, DataFrame.take() is classified as an action because it forces the execution of the transformations that were applied to the DataFrame and returns a specific result, making it the only operation in the list that triggers the computation and produces an output immediately.